Machine Learning for Arbitrary Single-Qubit Rotations on an Embedded Device

TL;DR

This paper introduces a machine learning approach for synthesizing single-qubit gates on embedded quantum devices, combining simulation-based modeling and hardware fine-tuning to improve gate performance.

Contribution

It presents a multi-stage method including simulation-based model bootstrapping and a novel adapted randomized benchmarking algorithm for hardware optimization.

Findings

Effective gate fidelity improvement demonstrated in simulations

Resource-efficient deployment techniques developed for embedded devices

Method adaptable to various quantum computing architectures

Abstract

Here we present a technique for using machine learning (ML) for single-qubit gate synthesis on field programmable logic for a superconducting transmon-based quantum computer based on simulated studies. Our approach is multi-stage. We first bootstrap a model based on simulation with access to the full statevector for measuring gate fidelity. We next present an algorithm, named adapted randomized benchmarking (ARB), for fine-tuning the gate on hardware based on measurements of the devices. We also present techniques for deploying the model on programmable devices with care to reduce the required resources. While the techniques here are applied to a transmon-based computer, many of them are portable to other architectures.